LLMs Are Complicated Now

It's the bitter-lesson to feature-engineering lifecycle.

When a technique or technology is new people are making massive gains by just applying it to some use case, or gathering more data for training, or giving it more resources.

As time goes on those "bitter lesson" gains start to hit the shallow part of the logistic curve and companies have to start investing more and more effort into engineering for each small, incremental gain.

One thing that makes LLMs complicated in production is that they're stateless — every call starts from zero. The complexity compounds when you need agents to maintain context across sessions and models. That's a layer that's largely missing from most stacks today.

Why didn't this author compare Llama 3 with GLM 5.2 (released 1 week ago) which is a more standard attention based LLM? To compare 2 separate families of LLMs and then pointing out that they are different is not a surprising result and detracts from the point the author is trying to make.

https://sebastianraschka.com/llm-architecture-gallery/?compa...

If you look at it, the diagrams are very similar, but the main differences are that the feedforward is replaced with a MoE (router to multiple feedforwards) and the model has a different attention implementation.

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